

import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
lowercase			:				List[str]   =     logging.get_logger(__name__)

lowercase			:				str   =     {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase			:				List[Any]   =     [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							for attribute in key.split('''.'''		):
														A      :					Optional[Any]      		=					getattr(lowerCamelCase_     ,				lowerCamelCase_		)

							if weight_type is not None:
														A      :					int      		=					getattr(lowerCamelCase_     ,				lowerCamelCase_		).shape
							else:
														A      :					List[Any]      		=					hf_pointer.shape

							assert hf_shape == value.shape, (
							    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
							    F' {value.shape} for {full_name}'
							)

							if weight_type == "weight":
														A      :					List[str]      		=					value
							elif weight_type == "weight_g":
														A      :					Optional[Any]      		=					value
							elif weight_type == "weight_v":
														A      :					Tuple      		=					value
							elif weight_type == "bias":
														A      :					List[str]      		=					value
							else:
														A      :					Any      		=					value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					str      		=					[]
							A      :					Optional[Any]      		=					fairseq_model.state_dict()

							A      :					str      		=					hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														A      :					Dict      		=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				hf_model.config.feat_extract_norm == '''group'''     ,				)
																					A      :					Optional[int]      		=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('''w2v_model.'''		)[-1] == name.split('''.'''		)[0]:
																																			A      :					str      		=					True
																																			if "*" in mapped_key:
																																										A      :					str      		=					name.split(lowerCamelCase_		)[0].split('''.'''		)[-2]
																																										A      :					Dict      		=					mapped_key.replace('''*'''     ,				lowerCamelCase_		)
																																			if "weight_g" in name:
																																										A      :					List[Any]      		=					'''weight_g'''
																																			elif "weight_v" in name:
																																										A      :					List[str]      		=					'''weight_v'''
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										A      :					Union[str, Any]      		=					'''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										A      :					Dict      		=					'''weight'''
																																			else:
																																										A      :					List[str]      		=					None

																																			set_recursively(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		)
																												continue
														if not is_used:
																					unused_weights.append(lowerCamelCase_		)

							logger.warning(F'Unused weights: {unused_weights}'		)






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					Tuple      		=					full_name.split('''conv_layers.'''		)[-1]
							A      :					List[str]      		=					name.split('''.'''		)
							A      :					Dict      		=					int(items[0]		)
							A      :					Dict      		=					int(items[1]		)

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
																					)
																					A      :					List[Any]      		=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
																					)
																					A      :					Any      		=					value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
																					    " found."
																					)
																					A      :					int      		=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F'{full_name} has size {value.shape}, but'
																					    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
																					)
																					A      :					Tuple      		=					value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
							else:
														unused_weights.append(lowerCamelCase_		)






@torch.no_grad()
def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_=None		):
							# load the pre-trained checkpoints
							A      :					List[Any]      		=					torch.load(lowerCamelCase_		)
							A      :					Any      		=					WavLMConfigOrig(checkpoint['''cfg''']		)
							A      :					Any      		=					WavLMOrig(lowerCamelCase_		)
							model.load_state_dict(checkpoint['''model''']		)
							model.eval()

							if config_path is not None:
														A      :					Any      		=					WavLMConfig.from_pretrained(lowerCamelCase_		)
							else:
														A      :					Optional[Any]      		=					WavLMConfig()

							A      :					Tuple      		=					WavLMModel(lowerCamelCase_		)

							recursively_load_weights(lowerCamelCase_     ,				lowerCamelCase_		)

							hf_wavlm.save_pretrained(lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Tuple   =     argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		lowercase			:				Dict   =     parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_pytesseract_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor







class 					__lowercase  (						unittest.TestCase				):





							"""simple docstring"""

							def __init__(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=7    ,						__UpperCAmelCase=3    ,						__UpperCAmelCase=18    ,						__UpperCAmelCase=30    ,						__UpperCAmelCase=4_00    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						)		->       int:
														A      :					Optional[Any]      		=					size if size is not None else {'''height''': 18, '''width''': 18}
														A      :					Dict      		=					parent
														A      :					Any      		=					batch_size
														A      :					Dict      		=					num_channels
														A      :					Optional[Any]      		=					image_size
														A      :					Dict      		=					min_resolution
														A      :					Optional[int]      		=					max_resolution
														A      :					Any      		=					do_resize
														A      :					List[Any]      		=					size
														A      :					List[Any]      		=					apply_ocr






							def       snake_case			(					self						)		->       Tuple:
														return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class 					__lowercase  (						_SCREAMING_SNAKE_CASE		,       unittest.TestCase				):





							"""simple docstring"""

							UpperCAmelCase_						:		Union[str, Any]								=	LayoutLMvaImageProcessor if is_pytesseract_available() else None




							def       snake_case			(					self						)		->       int:
														A      :					Optional[Any]      		=					LayoutLMvaImageProcessingTester(self						)




							@property
							def       snake_case			(					self						)		->       Optional[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()




							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Tuple      		=					self.image_processing_class(**self.image_processor_dict						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''do_resize'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''size'''						)						)
														self.assertTrue(hasattr(__UpperCAmelCase    ,						'''apply_ocr'''						)						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														A      :					Any      		=					self.image_processing_class.from_dict(self.image_processor_dict						)
														self.assertEqual(image_processor.size    ,						{'''height''': 18, '''width''': 18}						)

														A      :					int      		=					self.image_processing_class.from_dict(self.image_processor_dict    ,						size=42						)
														self.assertEqual(image_processor.size    ,						{'''height''': 42, '''width''': 42}						)




							def       snake_case			(					self						)		->       List[str]:
														pass




							def       snake_case			(					self						)		->       List[str]:
														# Initialize image_processing
														A      :					Optional[Any]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PIL images
														A      :					Union[str, Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						Image.Image						)

														# Test not batched input
														A      :					str      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						)
														self.assertEqual(
														    encoding.pixel_values.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														self.assertIsInstance(encoding.words    ,						__UpperCAmelCase						)
														self.assertIsInstance(encoding.boxes    ,						__UpperCAmelCase						)

														# Test batched
														A      :					int      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Union[str, Any]:
														# Initialize image_processing
														A      :					Any      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random numpy tensors
														A      :					Optional[Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						numpify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						np.ndarray						)

														# Test not batched input
														A      :					Dict      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														# Test batched
														A      :					int      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)




							def       snake_case			(					self						)		->       Optional[int]:
														# Initialize image_processing
														A      :					Optional[Any]      		=					self.image_processing_class(**self.image_processor_dict						)
														# create random PyTorch tensors
														A      :					Optional[Any]      		=					prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=__UpperCAmelCase    ,						torchify=__UpperCAmelCase						)
														for image in image_inputs:
																					self.assertIsInstance(__UpperCAmelCase    ,						torch.Tensor						)

														# Test not batched input
														A      :					List[str]      		=					image_processing(image_inputs[0]    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)

														# Test batched
														A      :					Dict      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						).pixel_values
														self.assertEqual(
														    encoded_images.shape    ,						(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size['''height'''],
														        self.image_processor_tester.size['''width'''],
														    )    ,						)






							def       snake_case			(					self						)		->       Any:
														# with apply_OCR = True
														A      :					Dict      		=					LayoutLMvaImageProcessor()

														from datasets import load_dataset

														A      :					Dict      		=					load_dataset('''hf-internal-testing/fixtures_docvqa'''    ,						split='''test'''						)

														A      :					Any      		=					Image.open(ds[0]['''file''']						).convert('''RGB'''						)

														A      :					List[Any]      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						)

														self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 2_24, 2_24)						)
														self.assertEqual(len(encoding.words						)    ,						len(encoding.boxes						)						)

														# fmt: off
														# the words and boxes were obtained with Tesseract 4.1.1
														A      :					Tuple      		=					[['''11:14''', '''to''', '''11:39''', '''a.m''', '''11:39''', '''to''', '''11:44''', '''a.m.''', '''11:44''', '''a.m.''', '''to''', '''12:25''', '''p.m.''', '''12:25''', '''to''', '''12:58''', '''p.m.''', '''12:58''', '''to''', '''4:00''', '''p.m.''', '''2:00''', '''to''', '''5:00''', '''p.m.''', '''Coffee''', '''Break''', '''Coffee''', '''will''', '''be''', '''served''', '''for''', '''men''', '''and''', '''women''', '''in''', '''the''', '''lobby''', '''adjacent''', '''to''', '''exhibit''', '''area.''', '''Please''', '''move''', '''into''', '''exhibit''', '''area.''', '''(Exhibits''', '''Open)''', '''TRRF''', '''GENERAL''', '''SESSION''', '''(PART''', '''|)''', '''Presiding:''', '''Lee''', '''A.''', '''Waller''', '''TRRF''', '''Vice''', '''President''', '''“Introductory''', '''Remarks”''', '''Lee''', '''A.''', '''Waller,''', '''TRRF''', '''Vice''', '''Presi-''', '''dent''', '''Individual''', '''Interviews''', '''with''', '''TRRF''', '''Public''', '''Board''', '''Members''', '''and''', '''Sci-''', '''entific''', '''Advisory''', '''Council''', '''Mem-''', '''bers''', '''Conducted''', '''by''', '''TRRF''', '''Treasurer''', '''Philip''', '''G.''', '''Kuehn''', '''to''', '''get''', '''answers''', '''which''', '''the''', '''public''', '''refrigerated''', '''warehousing''', '''industry''', '''is''', '''looking''', '''for.''', '''Plus''', '''questions''', '''from''', '''the''', '''floor.''', '''Dr.''', '''Emil''', '''M.''', '''Mrak,''', '''University''', '''of''', '''Cal-''', '''ifornia,''', '''Chairman,''', '''TRRF''', '''Board;''', '''Sam''', '''R.''', '''Cecil,''', '''University''', '''of''', '''Georgia''', '''College''', '''of''', '''Agriculture;''', '''Dr.''', '''Stanley''', '''Charm,''', '''Tufts''', '''University''', '''School''', '''of''', '''Medicine;''', '''Dr.''', '''Robert''', '''H.''', '''Cotton,''', '''ITT''', '''Continental''', '''Baking''', '''Company;''', '''Dr.''', '''Owen''', '''Fennema,''', '''University''', '''of''', '''Wis-''', '''consin;''', '''Dr.''', '''Robert''', '''E.''', '''Hardenburg,''', '''USDA.''', '''Questions''', '''and''', '''Answers''', '''Exhibits''', '''Open''', '''Capt.''', '''Jack''', '''Stoney''', '''Room''', '''TRRF''', '''Scientific''', '''Advisory''', '''Council''', '''Meeting''', '''Ballroom''', '''Foyer''']]  # noqa: E231
														A      :					int      		=					[[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
														# fmt: on

														self.assertListEqual(encoding.words    ,						__UpperCAmelCase						)
														self.assertListEqual(encoding.boxes    ,						__UpperCAmelCase						)

														# with apply_OCR = False
														A      :					Union[str, Any]      		=					LayoutLMvaImageProcessor(apply_ocr=__UpperCAmelCase						)

														A      :					List[str]      		=					image_processing(__UpperCAmelCase    ,						return_tensors='''pt'''						)

														self.assertEqual(encoding.pixel_values.shape    ,						(1, 3, 2_24, 2_24)						)






